Member of Technical Staff (Backend/Infrastructure Engineer, Search)
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Get Started FreeAbout interviewing at Perplexity
One of the few AI startups with a fully published interview guide (perplexity.ai/hub/careers/interview-guide): online application (response within two weeks) → recruiter phone screen → a technical screen that for engineers is 'usually a standard technical programming interview' → a quickly-scheduled onsite of 4–5 interviews including a hiring-manager deep dive on past work and experience anecdotes → a final interview with a Perplexity founder or leader → decision within a week of the onsite. Coding leans Python and mixes LeetCode medium–hard with practical search-flavored tasks (ranking/filtering, concurrency, data handling); system design is AI-native (RAG pipelines, retrieval at scale, LLM serving cost/latency). Applicants are judged 'solely on merit and potential impact' and must show 'frontier knowledge and excellence in at least one area'; roles are broad by default with team matching happening during the onsite, every role — managers included — is hands-on, and building AI products isn't expected but fluency in using AI tools is required. In-person 4 days/week near an office; remote is case-by-case.
Read the full Perplexity interview process →Description
Perplexity is looking for an Infrastructure Engineer to own and improve the backend systems behind our latency-sensitive search stack. You’ll work across high-QPS Rust and Go services, distributed retrieval systems, cloud infrastructure, observability, and deployment tooling.
This role combines infrastructure expertise with software engineering. You’ll take systems from design through production operation and use AI-native tools where they meaningfully improve engineering and operational workflows
Responsibilities
Build and operate scalable, low-latency infrastructure for search serving and retrieval.
Design and improve distributed backend systems that handle high request volumes.
Debug and optimize Linux systems, containers, networking, and production services.
Improve reliability through observability, capacity planning, performance profiling, and incident response.
Design, deploy, and operate cloud-native systems, primarily on AWS and Kubernetes.
Build internal tools and automation for development, testing, debugging, and infrastructure operations.
Improve CI/CD pipelines, release processes, and production rollout safety.
Contribute directly to product codebases across Rust, Go, and other systems languages.
Own systems end to end, from architecture and implementation to deployment and production operation.
Requirements
Strong experience with cloud infrastructure, distributed systems, and automation.
Deep understanding of Linux internals, performance analysis, and production debugging.
Experience building or operating latency-sensitive, high-throughput backend systems.
Experience with containers, orchestration, observability, and infrastructure as code.
Experience building or maintaining CI/CD systems and release tooling.
Fluency in at least one systems language, such as Rust, Go, C++, or Java.
Comfort working across infrastructure and application-level code.
Strong ownership and the ability to operate effectively in a fast-moving environment.
Nice To Have
Experience with search, retrieval, ranking, or other large-scale data-serving systems.
Experience operating Rust services in production.
Experience with AWS, Kubernetes, and modern observability systems.
Experience with profiling, capacity planning, and performance optimization.
Experience building AI-assisted developer or operational tooling.